Dataset.
To demonstrate the capabilities of the model, we conduct experiments on three special LDL datasets (Yeast-alpha, Human Gene, and a synthetic dataset).
These datasets are all small-scale feature spaces, as shown in Table REF .
The synthetic data is based on the minist dataset and the feature space is flattened with the help of an \(\texttt {Flatten}\)  operator, after which the feature space (28 \(\times \)  28) is reduced to 28 using PCA.
The label space is unsampled by building a Gaussian function (mean is the class of handwriting and variance is 0.5).
When evaluating the model performance, we used the six metrics proposed by [1]}, which are Chebyshev distance (Chebyshev \(\downarrow \) ), Clark distance (Clark \(\downarrow \) ), Canberra distance (Canberra \(\downarrow \) ), KL divergence (K-L \(\downarrow \) ), Cosine similarity (Cosine \(\uparrow \) ), and Intersection similarity (Intersection \(\uparrow \) ), where \(\uparrow \)  represents the bigger the better, and \(\downarrow \)  represents the smaller the better.
